A Statisical Word-Level Translation Model for Comparable Corpora
نویسنده
چکیده
In this paper, we present a model of statistical word-level mapping for comparable corpora. The approach is based on the assumption that if two terms have close distributional profiles, their corresponding translations’ distributional profiles should be close in a comparable corpus. The proposed model is described. A preliminary investigation on intralanguage comparable corpora is laid out. The preliminary results are >92% accurate, suggesting the feasibility of the model. The model needs to undergo some improvements and should be tested cross linguistically before assessing its significance.
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Data used for training statistical machine translation method are usually prepared from three resources: parallel, non-parallel and comparable text corpora. Parallel corpora are an ideal resource for translation but due to lack of these kinds of texts, non-parallel and comparable corpora are used either for parallel text extraction. Most of existing methods for exploiting comparable corpora loo...
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تاریخ انتشار 2006